Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning by Meiyue Shen, Yine Zhang, Runqing Zhan, Tingwei Du, Peixuan Shen, Xiaochuan Lu, Shengnan Liu, Rongrong Guo, Xiaoli Shen

“…Subsequently, six machine learning models were constructed, including random forest, decision tree, gradient boosting decision tree, k-nearest neighbor, support vector machine, and AdaBoost algorithms. Feature importance analysis, partial dependence plot, and shapley additive explanations were integrated to enhance the interpretability of the CVD prediction model. …”
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Enhancing furcation involvement classification on panoramic radiographs with vision transformers by Xuan Zhang, Enting Guo, Xu Liu, Hong Zhao, Jie Yang, Wen Li, Wenlei Wu, Weibin Sun

“…The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions. Conclusion DL algorithms can automatically classify FI using readily accessible panoramic images. …”
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msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis by Philippa Spangenberg, Sebastian Bessler, Lars Widera, Jenny Bottek, Mathis Richter, Stephanie Thiebes, Devon Siemes, Sascha D. Krauß, Lukasz G. Migas, Siva Swapna Kasarla, Prasad Phapale, Jens Kleesiek, Dagmar Führer, Lars C. Moeller, Heike Heuer, Raf Van de Plas, Matthias Gunzer, Oliver Soehnlein, Jens Soltwisch, Olga Shevchuk, Klaus Dreisewerd, Daniel R. Engel

“…Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from raw data import to analytical visualisation along with state-of-the-art and self-developed algorithms into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. …”
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Cardiovascular Disease Screening in Primary School Children by Alena Bagkaki, Fragiskos Parthenakis, Gregory Chlouverakis, Emmanouil Galanakis, Ioannis Germanakis

“…Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). …”
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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”
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Dynamic analysis and optimal control of a hybrid fractional monkeypox disease model in terms of external factors by Saima Rashid, Abdul Bariq, Ilyas Ali, Sobia Sultana, Ayesha Siddiqa, Sayed K. Elagan

“…We remark that in various time scale domains $$\mathbb {N}_{\ell }$$ , the investigated discrete formulations will be $$\rho ^{2}$$ -nonincreasing or $$\rho ^{2}$$ -nondecreasing by examining $$\rho$$ -monotonicity formulations and the basic properties of the suggested operator. Algorithms are constructed in the discrete generalized Mittag–Leffler (GML) kernel for mathematical simulations, emphasizing the effects of the infection resulting from multiple factors. …”
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Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty by Peijun Shi, Guojian Ni, Rifeng Jin, Haibo Wang, Jinsong Wang, Zhongwei Sun, Guizhi Qiu

“…We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. …”
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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study by Yunzhen Ye, Yu Xiong, Qiongjie Zhou, Jiangnan Wu, Xiaotian Li, Xirong Xiao

“…Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. …”
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Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities by Thien B. Nguyen-Tat, Tran Quang Hung, Pham Tien Nam, Vuong M. Ngo

“…This study provides a thorough evaluation of the performance of different preprocessing methods and deep learning algorithms across commonly used medical imaging modalities. …”
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Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP by Zeying Wen, Xiaohe Gao, Qingxia Wu, Jianwei Yang, Jian Sun, Keliu Wu, Hongfei Zhao, Ruihua Wang, Yanmei Li

“…Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithms were used for dimensionality reduction and selection of radiomics features. …”
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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…New hydrological insights for the region: Models that integrate data discretization outperformed those using numerical normalization, showing higher recall scores and greater stability across selected algorithms (linear regression, support vector machine, random forest, and decision tree). …”
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Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data by Karla Gabriele Florentino da Silva, Paulo Victor de Magalhães Rozatto, Kaio de Oliveira e Sousa, Lucas Dias Hudson, Artur Welerson Sott Meyer, Alemilson Fabiano Silva, Igor Tibiriçá Mendes, Alex Rodrigues Borges, Leandro Elias Morais, Luiz Maurílio da Silva Maciel, Saulo Moraes Villela, Helio Pedrini, Marcelo Bernardes Vieira

“…This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. …”
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Neuroeconomically dissociable forms of mental accounting are altered in a mouse model of diabetes by Chinonso A. Nwakama, Romain Durand-de Cuttoli, Zainab M. Oketokoun, Samantha O. Brown, Jillian E. Haller, Adriana Méndez, Mohammad Jodeiri Farshbaf, Y. Zoe Cho, Sanjana Ahmed, Sophia Leng, Jessica L. Ables, Brian M. Sweis

“…These findings suggest that complex relationships between metabolic dysfunction and dissociable valuation algorithms underlying unique cognitive heuristics and sensitivity to opportunity costs can disrupt distinct computational processes leading to comorbid psychiatric vulnerabilities.…”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…The research offers policymakers, transportation authorities, and safety advocates practical insights by utilizing sophisticated machine-learning algorithms and integrating multiple datasets. Road crash fatalities can be decreased and safer transportation systems can be established by using the predictive models that have been created as a proactive tool for identifying high-risk regions and allocating resources for targeted improvements. …”
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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

“…This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. …”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park

“…However, prediction models are sensitive to the size and distribution of the data they are trained on. ML algorithms rely heavily on vast quantities of training data to make accurate predictions. …”
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In Silico Identification of New Anti-SARS-CoV-2 Main Protease (Mpro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hi... by Harrison Onyango, Patrick Odhiambo, David Angwenyi, Patrick Okoth

“…In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 Mpro. …”
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New prognostic model in patients with advanced urothelial carcinoma treated with second-line immune checkpoint inhibitors by Ernest Choy, Yohann Loriot, Fabio Calabrò, Cora N Sternberg, Guillermo De Velasco, Daniel Castellano, Roubini Zakopoulou, Aristotelis Bamias, Nicholas James, Axel Merseburger, F Lopez-Rios, Mario Kramer, Kimon Tzannis

“…Nevertheless, existing algorithms may not be sufficient following the introduction of immunotherapy. …”
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MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions by Maham Misbah, Misha Urooj Khan, Zeeshan Kaleem, Ali Muqaibel, Muhamad Zeshan Alam, Ran Liu, Chau Yuen

“…The proposed solution also outperformed several other state-of-the-art algorithms exists in the literature.…”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
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